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Record W2003746460 · doi:10.1109/cjece.2014.2327951

Power Factor Control in a Wind Energy Conversion System via Synchronous Generator Excitation

2014· article· en· W2003746460 on OpenAlexaffvenue
Murad Jafari, Miteshkumar Popat, Bin Wu

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPower factorWind powerPermanent magnet synchronous generatorControl theory (sociology)Maximum power point trackingRectifier (neural networks)Generator (circuit theory)Power controlPower (physics)Power optimizerAC powerMaximum power principleComputer scienceEngineeringInverterElectrical engineeringPhysicsControl (management)Voltage

Abstract

fetched live from OpenAlex

This paper proposes a novel control technique to improve the power factor over a wide range of wind speeds in a wind energy conversion system (WECS) using a current source inverter (CSI) and an electrically excited synchronous generator. The system consists of diode rectifier on the generator side, while a pulsewidth modulated CSI is on the grid side. In the proposed control scheme, the generator excitation is controlled with the wind speed to improve the grid power factor, while the control freedoms of the CSI are used to regulate the power output of the WECS to meet maximum power point tracking of wind energy. Theoretical analysis is conducted to investigate the feasibility and limits of this approach. The simulation model for the proposed WECS is developed, and the simulation results confirm the validity of the theoretical analysis. With the proposed power factor control scheme, improvements of grid-side power factor are achieved over a wider range of wind speeds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.002
GPT teacher head0.128
Teacher spread0.126 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2014
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Electrical and Computer EngineeringSame topicWind Turbine Control SystemsFrench-language works237,207